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claritylab/zero-shot-explicit-bi-encoder

sourceHugging Facemitupdated 3y agoView on Hugging Face

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Zero-shot Explicit Bi-Encoder

This is a sentence-transformers model. It was introduced in the Findings of ACL'23 Paper Label Agnostic Pre-training for Zero-shot Text Classification by *Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars*. The code for training and evaluating this model can be found here.

Model description

This model is intended for zero-shot text classification. It was trained under the dual encoding classification framework via explicit training with the aspect-normalized UTCD dataset.

—Finetuned from model: `bert-base-uncased`

Usage

You can use the model like this:

python

>>> from sentence_transformers import SentenceTransformer, util as sbert_util
>>> model = SentenceTransformer(model_name_or_path='claritylab/zero-shot-explicit-bi-encoder')

>>> text = "I'd like to have this track onto my Classical Relaxations playlist."
>>> labels = [
>>>     'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
>>>     'Search Screening Event'
>>> ]

>>> text_embed = model.encode(text)
>>> label_embeds = model.encode(labels)
>>> scores = [sbert_util.cos_sim(text_embed, lb_embed).item() for lb_embed in label_embeds]
>>> print(scores)

[
  0.53502357006073,
  0.051911696791648865,
  0.0546676367521286,
  0.5633962750434875,
  0.28765711188316345,
  0.17751818895339966,
  0.18489906191825867
]